Will anybody use AI as much as coders?

By Gammatek ISPL , Industrial Systems & Compliance Analyst at Gammatek ISPL

Last updated: August 2026 | 11 min read

Gammatek ISPL advises manufacturing, chemical, and pharmaceutical plants on safety, compliance, and industrial technology adoption at Gammatek ISPL, drawing on direct work with plant engineering teams evaluating AI-driven tools. This piece is independent analysis, not sponsored by any AI vendor named below.

Two professions, two very different relationships with AI — and the gap is closing faster than most people expect.

Why This Matters

Software developers now use AI tools daily — writing code, debugging, reviewing pull requests, even architecting systems, often with an AI assistant open in a second window the entire workday. Plant engineers, running the machinery that actually makes the physical world work, mostly don't. Not because the technology doesn't exist for them — it does — but because the entire environment they work in makes AI adoption fundamentally harder, slower, and riskier to get wrong.

That gap matters beyond curiosity. If you run, manage, or invest in an industrial operation, the pace at which your engineering team can safely adopt AI is becoming a real competitive variable — for uptime, for compliance, and for the speed at which problems get caught before they become expensive ones. Understanding why the gap exists is the first step to closing it responsibly.

The Coder's Advantage: A Forgiving Environment

Software development is, relatively speaking, a low-stakes environment for experimentation. If an AI-generated function is wrong, a developer sees it fail in a test suite, in a staging environment, or worst case, in a bug report — rarely in physical injury or regulatory violation. This forgiving feedback loop is the reason coding became AI's first mainstream professional use case: mistakes are cheap, fast to detect, and easy to roll back.

Plant engineering has almost none of these properties. A wrong recommendation from an AI system monitoring a chemical process, a pressure valve, or a production line isn't caught by a test suite — it's caught by an alarm, a shutdown, or worse. There is no "staging environment" for a live manufacturing floor. This single structural difference explains more about the adoption gap than any argument about industrial workers being slower to adapt.

Where Industrial AI Adoption Actually Stands in 2026

Function	AI Adoption Level	Primary Use Today
Software development	Very high (daily use, mainstream)	Code generation, debugging, code review
Predictive maintenance (industrial)	Moderate, growing fast	Anomaly detection, failure prediction
Compliance/audit documentation	Low-moderate	Automated logging, report generation
Live process control (OT)	Low	Alerting/recommendation only — rarely autonomous
Physical safety monitoring	Low-moderate	Computer vision for PPE/hazard detection

In our own work advising plant operators, the clearest pattern is this: AI adoption in industrial settings tracks almost exactly with how far removed the AI is from directly controlling physical outcomes. Predictive maintenance tools — which flag potential equipment failures without touching the machine itself — see the fastest adoption. Anything closer to live process control moves far more cautiously, and for good reason: regulatory frameworks like IEC 62443 and plant-specific safety protocols weren't built with autonomous AI decision-making in mind, and most plants aren't going to outrun their compliance obligations to chase a trend.

A Real Example: Where the Gap Shows Up in Practice

In plants we've worked with, the AI adoption curve tends to follow a predictable sequence: monitoring and alerting tools get adopted first (low risk, easy to layer on top of existing systems), followed by predictive maintenance recommendations (moderate risk, human still approves the action), with autonomous control staying firmly in "not yet" territory for the vast majority of operators — even ones that are otherwise aggressive early adopters of other technology.

This isn't reluctance for its own sake. It's a rational response to the fact that a false positive in a code review costs a developer a few minutes; a false positive — or worse, a false negative — in a plant safety system can cost far more. The engineers we've talked to aren't AI-skeptical; they're risk-calibrated in a way that coding culture, by comparison, often isn't.

Will the Gap Close? Three Things Have to Happen First

1. Explainability has to improve. A developer can read AI-generated code and judge it. A plant engineer overseeing a chemical process needs an AI recommendation they can audit and justify to a regulator — "the model said so" isn't an acceptable answer in a compliance review, and it shouldn't be.

2. Liability frameworks need to catch up. Software mistakes rarely trigger the kind of legal and regulatory exposure that an industrial incident does. Until insurance, liability, and regulatory frameworks clarify how AI-assisted decisions are treated, plants have a strong incentive to keep humans firmly in the loop.

3. Integration with existing compliance systems. AI tools that sit disconnected from a plant's actual audit trail and compliance documentation create more work, not less — someone still has to manually reconcile what the AI recommended against what compliance requires. Tools that plug directly into existing compliance and safety documentation workflows will see adoption far faster than standalone AI point-solutions.

The Honest Answer

Will plant engineers ever use AI as much as software developers do, hour for hour? Probably not in the same way — the nature of the work doesn't allow for the same trial-and-error rhythm, and it shouldn't. But "as much" isn't really the right measure. The more useful question is whether AI becomes as embedded in plant engineering workflows as it has in coding — even if it shows up differently: as a second set of eyes on equipment data, an assistant compiling audit documentation, or an early-warning system rather than an autonomous decision-maker.

On that measure, the trajectory is clearly upward — just on a timeline set by safety and compliance requirements, not by how fast the underlying technology can move.

How This Connects to What You're Already Managing

If your plant is already evaluating predictive maintenance tools like FixitX or thinking through how AI-assisted monitoring fits into your existing safety and compliance documentation, the adoption pattern above is exactly the one to plan around: start with monitoring and recommendation-layer AI, keep humans approving anything touching live process control, and make sure whatever tool you adopt integrates with your audit trail rather than sitting outside it.

See how Gammatek's compliance platform integrates AI-driven monitoring data directly into your audit documentation → (link to relevant Gammatek product page)